Sahha’s platform aims to look after the wellbeing of every individual. Keeping that in mind, we have made machine learning models and scores that capture both the mental state of the user as well as the physical state.
Sahha as a platform features a number of other products from collecting raw device data through the Sahha SDK and collecting other health insights models to the Sahha Dashboard for developer tools and data analytics
Sahha provides powerful mental health scores− which analyze the user’s mental state, well-being scores− which capture the user’s physical and digital behaviour and resilience scores− which measure the resilience towards developing mental health conditions in the near future. All scores are designed with explainability in mind to help you incentivize behaviour change and tracking for your customers.
Together, these machine learning models provide you with a holistic view of the individual. In this documentation you can find everything you need to integrate these models inside of your offering, best practices on how to get the most value out of the models, as well as the science behind how they were created.
Health Data & Digital Phenotyping
At its core, Sahha is a platform for developers and organisations wanting to empower their users/customers with innovative and personalised healthcare experiences through data.
When integrated into an application, Sahha collects data passively from smartphones and through wearable devices that are connected to smartphones (such as through Apple HealthKit). This data is called digital phenotypes . Once collected, this data is then regularly analyzed and interpretted as different behavioral, mental and physical health insights that can be used within any application.
What are digital-phenotypes?
Digital phenotypes refer to the unique and characteristic patterns of digital interactions, behaviors, and data generated by individuals when using digital devices, such as smartphones, wearables, or computer applications. These digital behaviors can encompass a wide range of activities, including: sleep data, step count, device usage patterns and biomarkers (such as blood glucose, heart-rate and others). Sahha focuses on what we call "non-invasive" digital-phenotypes, we don't collect personally identifyable information and our technology requires native device permissions to be sent to individuals whose data is being collected and accepted by them before it can be.
Digital phenotyping aims to capture and analyze these behaviors to gain a deeper understanding of an individual's health, mental state, and overall well-being. This information can be valuable in healthcare, mental health, and wellness applications, allowing for more personalized and data-driven approaches to care and support. However, it also raises important privacy and ethical considerations related to the collection and use of digital behavior data.
What is the standard Sahha schema?
View the schema page for an example of how Sahha turns digital-phenotypes such as sleep, steps and age data into a rich analysis and prediction about an individual's stress:
What data and sensors does Sahha use?
We're constantly adding new data sources and we collect data from various device sensors. When integrating Sahha you can specify which sensor data you'd like to use.
Some sensors are not available on all platforms.
|StepCount, FlightsClimbed, MoveTime, StandTime, ExerciseTime, ActivitySummary
|HeartRate, RestingHeartRate, HeartRateVariabilityRmssd
|HeartRate, RestingHeartRate, WalkingHeartRateAverage, HeartRateVariabilitySDNN
|BloodPressureSystolic, BloodPressureDiastolic, BloodGlucose
|OxygenSaturation, RespiratoryRate, Vo2Max
|VO2Max, OxygenSaturation, RespiratoryRate
|ActiveCaloriesBurned, BasalMetabolicRate, TotalCaloriesBurned
|ActiveEnergyBurned, BasalEnergyBurned, TimeInDaylight
|Height, Weight, BodyFat, BodyWaterMass, BoneMass, LeanBodyMass
|Height, Weight, LeanBodyMass, BodyMassIndex, BodyFat, WaistCircumference
|BodyTemperature, BasalBodyTemperature, AppleSleepingWristTemperature
Sahha's Models & Scores
Sahha’s core analysis offerings can be broken down in to three different offerings:
|🧠 Mental Health Scores
|Analyzes how the current behavior of the user aligns with those with mental health conditions
|Replace the need to ask the user to take a clinical survey by passively tracking the user. Provide offerings on the current state prediction of the user and incentivize behavioral change.
|Depression, Anxiety, Stress
|🦾 Resilience Scores
|Analyzes how the behavior of the user makes them more or less likely to develop mental health conditions in the future
|Provide interventions based on the users level of resilience and incentivize behavioral change.
|Resilience to depression, anxiety, stress
|🌱 Well-being Scores
|Quantifies the physical well-being of the user through a Wellbeing score. Sub-scores provide a more specific analysis through Activity score, Sleep score and Digital score.
|These white label scores enable you to utilize the scores specific to your offering, providing specific experiences around the scores that matter most to you and your users.
|Overall Well-being score, Activity score, Sleep score, Digital Well-being score